AutoCompBD: Autonomic Computing and Big Data platforms

Pop, Florin; Dobre, Ciprian; Costan, Alexandru

doi:10.1007/s00500-017-2739-8

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Most platforms deal with data (ergo the term is Big Data and not Big Info), and have the common hurdle of unifying data sources in a simple matter that can be read (Pop, 2017). With the proliferation of metadata and tagging, bot technology has substantially advanced in the past few years, where algorithms scan through data and deliver summaries based on the frequency, order, and the relation of components with one another.…”

Section: Machine Learning and Smart Curationmentioning

confidence: 99%

Assessing digital strategies and tools for legacy publishers: What works? What fails? And What's next?

Christie¹

2023

Preprint

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This paper will first outline the importance of transitioning to the digital era for legacy publishers, defined here as publishers that existed before the rise of the Information Age and the current dominance of digital technologies in the 21st century. Particular focus is paid to academic publishers, catalogues, and journals rather than publishers in education and trade. The parameters of digital transition, the choices a legacy publisher may face, and the problems that challenge legacy publishers are discussed and put into context in regards to a legacy publisher’s role in the digital age. Finally, an examination of different tools used in digital native companies is performed and their usefulness for legacy publishers are analyzed.

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Section: Machine Learning and Smart Curationmentioning

confidence: 99%

Assessing digital strategies and tools for legacy publishers: What works? What fails? And What's next?

Christie¹

2023

Preprint

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“…Otherwise, this processing may lose its value over time. Parallel processing is based on parallel systems which are made up of processors, a hierarchy of memories and interconnecting networks (Pop et al , 2017; Wah, 2008; Inderpal, 2013). Parallel processing introduces models and architectures for performing multiple tasks within a single compute node or group of tightly coupled nodes with homogeneous devices (Conti, 2015).…”

Section: Related Workmentioning

confidence: 99%

“…These technologies allow to rapidly increase processor speed and power efficiency. In addition, the processing of big data is speeded up by the distributed systems used to enable the exchange of data between compute nodes (Pop et al , 2017). In parallel processing, several processors cooperate to solve a problem, which reduces the processing time, because several operations can be performed simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Distributed and Parallel Machine Learning algorithms for big data mining processing

Djafri

2021

DTA

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PurposeThis work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.Design/methodology/approachIn the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.FindingsThe authors got very satisfactory classification results.Originality/valueDDPML system is specially designed to smoothly handle big data mining classification.

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“…In the areas of big data analysis, Jayasena et al [23] propose the ant colony optimization (ACO) algorithm for efficient resource allocation in infrastructure layer. Pop et al [24] orient on computer and information advances aiming to develop and optimize advanced system software, networking, and data management components to cope with big data processing and the introduction of autonomic computing capabilities for the supporting large-scale platforms. Traditional alert log analysis can no longer fulfil the need in discovering the relationship between anomalous data and landslides.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open‐Pit Mine Slope

Jiang

Lian

et al. 2018

Complexity

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With the diversification of pit mine slope monitoring and the development of new technologies such as multisource data flow monitoring, normal alert log processing system cannot fulfil the log analysis expectation at the scale of big data. In order to make up this disadvantage, this research will provide an ensemble prediction algorithm of anomalous system data based on time series and an evaluation system for the algorithm. This algorithm integrates multiple classifier prediction algorithms and proceeds classified forecast for data collected, which can optimize the accuracy in predicting the anomaly data in the system. The algorithm and evaluation system is tested by using the microseismic monitoring data of an open-pit mine slope over 6 months. Testing results illustrate prediction algorithm provided by this research can successfully integrate the advantage of multiple algorithms to increase the accuracy of prediction. In addition, the evaluation system greatly supports the algorithm, which enhances the stability of log analysis platform.

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AutoCompBD: Autonomic Computing and Big Data platforms

Cited by 6 publications

References 12 publications

Assessing digital strategies and tools for legacy publishers: What works? What fails? And What's next?

Assessing digital strategies and tools for legacy publishers: What works? What fails? And What's next?

Dynamic Distributed and Parallel Machine Learning algorithms for big data mining processing

Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open‐Pit Mine Slope

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